How to Value Draft Picks vs. Active Players

What is a Draft Pick Worth in a Trade for an Active Player?

This article is being co-posted on Maple Leafs Hot Stove as well as on my own site, http://www.originalsixanalytics.com. Find me @michael_zsolt on twitter.

Many, many writers have touched on the concept of Draft Pick Value, myself included. Those who find it interesting are happy to talk about it for days, and those who don’t tend to steer clear pretty quickly. The one downfall of the work done to date is that almost all of it has focused on what draft picks are worth when traded for – you guessed it – other draft picks. In the spirit of the (now passed) trade deadline, I want to take a quick look at answering the following question:

How can we reasonably compare the value of a draft pick to the value of an active player?

To answer this, I will (i) introduce the concept of ‘absolute’ draft pick value, and (ii) go through an example, looking at the Leafs’ recent trade of Roman Polak and Nick Spaling to San Jose. My goal is not to conclude who ‘won’ the deal (I think that has already been decided), but rather to apply the concepts to a concrete example and give this analysis a bit more of a practical implication.

I want to make one caveat clear before comparing pick value and player value: This — and any other type of ‘valuation’ analysis — will always be an inexact approach and will not tell us the full picture. Teams don’t make decisions on deadline day because of abstract math; they make trades because they want to win, they have a specific spot to fill, and they believe that particular trade is the best way to fill it. The market dynamics of the trade deadline also have a huge impact in how trades go. This year, demand exceeded supply for defensemen, driving up the price of players like Roman Polak. Similarly, no one was really looking for a rental goalie, so James Reimer fetched far less than he is probably worth. As such, keep in mind this type of analysis should always be considered in conjunction with a wide range of other quantitative and qualitative factors.

(Relative) Draft Pick Value

As mentioned, previous work has largely focused on relative draft pick value – that is, what a pick is worth in a deal for other picks. These draft pick value charts often end up quantifying picks/future players in terms of some currency or ’unit’ that is hard to assign meaning to out of context. Relative draft pick value is most useful on the day of the draft, when many pick-for-pick trades are made and we know the exact selection number a pick relates to (as opposed to only knowing the round). However, in order to compare between draft picks and players, we need to compare players on the same metric — one that addresses the concept of absolute draft pick value.

Absolute Draft Pick Value

In his recent piece, Stephen Burtch took another big step forward in this area by laying out a number of simple and clear metrics in a concise table to illustrate some proxies for draft pick value. Here is his table:

burtch

I am a big fan of this table. Not only does Burtch introduce an absolute value metric for players (Expected Pts/GP), but he shows how long it takes for those players to become real contributors (Seasons until 150GP). He also connects it all to the Goals Above Replacement metric – another thing I am a big fan of. Later on in his article, Burtch also essentially splits teams into ‘buyers and sellers’ – a very useful lens through which to view the market dynamics on deadline day (e.g. availability or scarcity of particular assets, driving demand and price).

Although this chart is a great start for absolute pick value, I think we can go one step further. Having analyzed Expected Pts/GP for draft picks myself, there are two things I want to point out. The first, which we’re all aware of, is that it differs significantly by forwards and defensemen (Burtch no doubt recognizes this; he just wasn’t showing that level of detail in his table). Secondly, it has a survivorship bias – that is, over time, only the strongest players remain in the league and continue scoring points, thus driving up the averages over time. Thus, Pts/GP does not appropriately account for the probability that a player stays in the league at all.

Player Lifetime Production

While I recommend using as many value-metrics as possible to establish a well-rounded view, one of my own favourites for absolute pick value is Lifetime Production, calculated as the expected average cumulative points per player (e.g. total career points). This metric implicitly adjusts for players who never make it into the league at all, as the denominator in the equation is total players drafted rather than total games played. See below for two of my previous charts, showing this metric for both forwards and defensemen.

Lifetime Production - F

Lifetime Production - D

(Note: The chart sample is all players drafted between 2000 and 2004, and their subsequent playing histories over each of their first ten seasons in the league).

Although this data doesn’t separate the top three picks overall, who deserve their own echelon, you can still see some clear results:

  • Over their first ten seasons in the league, a top-10 overall pick should be considered to be worth ~170 total points if a defenseman is selected, or ~350 points if a forward is selected.
  • Depending on how soon that player really begins to contribute, (e.g. many players only are NHL regulars for 5-8 of their first ten seasons), top-10 overall defensemen come out as ~20-30 point per season players, and top-10 forwards come out as ~40-60 point per season players.

Now, this metric is also not perfect. It works well when used far in advance of the draft, when it is unclear what overall selection number a pick relates to. However, it can be a pretty high level approach when a team knows it is holding the 33rd overall pick, for example, which could be treated much the same as a late first round pick. Many will also rightfully point out that these are averages with huge distributions in results. In all rounds, there will be many players who will have 500-600+ points over these 10 seasons, and many who will have less than 10. As a result, these averages/expected values will only ever be one piece of the puzzle.

Case Study: TML trade Polak/Spaling for two second round picks

Lastly, I will try to illustrate this concept a little more clearly by looking at the Leafs’ recent trade of Polak and Spaling to San Jose in return for two second round picks. Here are the assets that changed hands in the deal:

Pic 1

Now, Raffi Torres was more of a cap offload by San Jose, who the Leafs have let remain at San Jose’s AHL affiliate (he’s also not playing for the rest of the season). As a result, let’s exclude him from the comparison. For simplicity’s sake, to stack up the remainder of the trade I have assumed TML uses the two picks to select one forward and one defenseman. Further, based on both Burtch’s and my charts above, second round picks only really begin to meaningfully contribute around their fifth season after being drafted. As such, you should consider the ‘lifetime production’ for these picks to be over approximately five or six active NHL seasons.

The table below summarizes the career points we can expect from these picks versus what could be expected from Roman Polak / Nick Spaling over each of their next five seasons.

Lifetime total

In order to try to show this on an apples-to-apples basis, I have assigned Polak/Spaling the value of their cumulative points over their last five seasons. Now, this is not exactly scientific, and should not be treated as a ‘trade-defining’ result. However, the chart does show an interesting finding: based on what can reasonably be expected from these picks over ~10 years after being drafted (which includes adjusting for their likelihood to succeed in the league at all), they will not necessarily be as productive as Polak/Spaling will be over each of their next five seasons, in the aggregate.

However, that is not the full story of course. Polak and Spaling are shown here in a somewhat generous view of what you could expect out of them for the next ~5 seasons. It does not discount their performance at all for declining with age, nor does it consider their moderate cap hits, as 27 and 29 year old players. Given that they are both Unrestricted Free Agents (UFAs) at the end of this year, San Jose may only actually realize the value of ‘one’ of the next five seasons from these two players. The table below adjusts this data to be shown on a per-season basis, rather than in aggregate, simply by dividing the last chart by five expected seasons:

Per season total

Looked at differently, although San Jose got a total of 32 ‘Pts/Season’ worth of production, the Sharks only have certainty that they acquired the tail end of the 2015-16 season (e.g. ~20 games) before these two players could walk away and sign with any team in the league.

At the same time, Toronto ‘only’ acquired 23 Pts/Season, but this will be spread out over five productive years. These will also be the prime years of those players’ careers, where their value is the highest, due to being a low cap hit while on Entry-level/UFA deals; Toronto has the players’ exclusive rights while they develop. Last, as has been touched on in the past, ideally Mark Hunter and company can actually increase the probability of turning these picks into higher-calibre players than average, given his and his team’s strong network and scouting capabilities.

A Note on Time Value

One final note before concluding: It is also worth pointing out that, given these picks are for 2017 and 2018, they are inherently less valuable than a pick for this upcoming draft, and I suspect that is a key reason that San Jose was willing to make this trade. Even rebuilding teams want to rebuild now, not 2-3 years from now.

To illustrate this concept, ask yourself if a second round draft pick in 2022 is worth the same as one in 2016? Standing here in 2016, it is not. The same logic applies to 2017 and 2018 picks, although to a lesser extent. Fortunately for Toronto, the Leafs have so many picks in 2016 that arguably they were looking for picks in later years in the first place. However, this willingness to accept a later date (plus the lack of supply of rental defensemen this year) likely helped the Leafs increase their yield in this trade significantly. For anyone interested, this ‘time value’ concept is directly borrowed from the world of corporate finance, where ‘discounting future cash flows’ (e.g. player production) is the foundation of assigning a value to a business.

Conclusion

To wrap up, I have hopefully heavily caveated that this analysis should not be considered scientific, the best, or even the only way to compare the value of draft picks to active players. What this hopefully does provide us, though, are some useful heuristics (rules of thumb) to keep in mind for future deals, which can be combined with all of the other methods available at our disposal to evaluate transactions. As a result, the next time you see a team toss around two 1st round picks (or equivalent players) for a long-term Phil Kessel-type player, or an short-term rental of an Andrew Ladd-type player, hopefully you walk away thinking about just how many hundreds of future points they are giving up down the road.

Advertisements

2015 Draft Day: How Hunter and Dubas May Have Out-Played the League (Part 2)

This article is being posted here as well as in parallel as a guest post at Maple Leaf Hotstove.

In my last post I shared my analysis on long term player performance and development based on draft round. As a follow-on to that article, I’d like to do two things: first, I’ll convert my last analysis into a relatively straightforward and ‘usable’ metric for draft pick value. Then, I’ll apply this metric to two short case studies in order to illustrate who won each of the Leafs draft day trades this past summer (hint: it wasn’t Ron Hextall or Jarmo Kekalainen…).

Draft Pick Value

The third and final objective from my original report posed the following question:

  • How much more valuable is a pick in the first round versus the other rounds? All things being equal, what should a pick from each round be worth in a trade?

Building on the analysis done by others mentioned in my last post, the chart below summarizes how I have approached ‘converting’ long term performance data into a relative draft pick value metric. To be clear: I am not proposing the values shown in this chart, rather, I hope to use this chart to illustrate the methodology I have applied across a number of metrics.

Games played data is one metric that can inform relative pick value by draft round

Pick Value Demo Chart 

  • The chart above shows how likely a player from each round is to play ~2+ seasons in his career, and by when he should be expected to do so
  • The chart then calculates how much more likely a player from each round is to pass 150 GP than the bottom cohort of rounds (e.g. average of Rounds 4-9) – shown as a multiple of those rounds
  • Thus, if we define a pick in rounds 4-9 as the ‘base unit’ (e.g. ‘1.0 units’), using the >150 GP threshold shows a third round pick to be worth 1.8 units, a second round pick being worth 2.4, an 11th-30th overall pick being worth 6.0, and a top 10 overall pick being worth 7.6

Applying this approach to multiple metrics will give us a more robust view of relative pick valueRelative Pick Value Chart

  • The table above shows the ‘Draft Value Units’ (working name) of a pick in each round across three metrics: >30 Pts, >100 Pts, and Avg Career Pts. In essence, ‘Draft Value Units’ are comparable to a currency with which teams can value and exchange draft picks
  • As mentioned – each round is shown as its multiple of the lowest group (Rounds 4-9) – and most of my attention going forward will be on the far right, highlighted column; also, all of the values of this chart are derived from the data shown in my last article
  • As Michael Schuckers and Stephen Burtch previously showed, this data suggests teams should use caution when trading their first and second round picks, as they can be worth many times more valuable than the other rounds
  • It is worth noting that Lifetime Production data can also shed light on ‘absolute’ pick value; e.g. in a trade for active players, a pick in the top 10 overall should be treated as if it has a career lifetime value of ~350+ points as a forward, or ~170+ points as a defensemen – something directly comparable to ‘remaining’ production in an active NHLer

Part of the goal of this exercise was to create a pick valuation methodology that is highly simple, and usable by many, regardless of their level of analytical sophistication. As someone very familiar with the world of corporate finance and valuations, I can tell you first hand that – despite financial firms using the ‘fanciest’, most complex valuation models you could imagine – the most effective of these models will often reduce complexity, rather than create it. All investors and bankers also know that valuation analysis is a ‘blunt’ tool, and it will never give you an exact, ‘true’, intrinsic value for a corporation or a stock (i.e. picture using an axe to carve a statue). I think the same thought process applies to this draft pick value methodology – it is directional, rather than exact – but hopefully it also intuitive to understand and apply. My general philosophy is that decision-makers do best when considering metrics that are reflective of the big picture, while simultaneously weighing those against the typical qualitative information they bring to the table, such as team needs, player skill, size, character, etc.

Now – let’s get into the deals.

Draft Day 2015 Deal #1 – Dubas and Hunter v. Ron Hextall of the Philadelphia Flyers

After seemingly endless conversations and hustling around the draft floor, Dubas and Hunter’s first trade of the day was with Philadelphia:

 TOR PHL Trade Chart

Now, based on the far right column in my ‘Draft Value Unit’ table above, we would think to assign the following values to these picks:

TOR PHL - 'Wrong' Pick Chart

Huge win for the Leafs, right?

Not necessarily. As we all know, and as Schuckers and Burtch’s analyses clearly demonstrate – all picks in each round are not created equal. The 11th overall pick is not equivalent to the 30th, even though the table above would be treating them as having the same value. Because of this, the Draft Value Units shown above would be most appropriate to use when a trade is done well in advance of a draft, and it is not known exactly which overall draft number a given pick will relate to. In order to make this metric meaningful to trading ‘known’ pick numbers, we will have to do some adjusting.

Applying ‘Draft Value Units’ directly to draft pick numbers will show some counter-intuitive results

Draft Value Units - Step Function

Instead, for individual pick numbers, we need to ‘fill in the gaps’ with a new curve, equivalent to the equation shown below

 DVU - Curve

  • Once we do know what pick numbers each team will have, we need to adjust the value of each pick appropriately
  • To do this, I have derived the solid, light blue line in the chart above, which best fits the original Draft Pick Values shown
  • This line shows what the appropriate Draft ‘Value’ is for a pick once we know the exact pick number that it relates to
  • The line was derived by looking at the (x,y) coordinates of each pick number and its respective Draft Value Units, after assigning the values in the first table shown to the mid-point of each round (e.g. 5th overall pick being worth ~11.1 DVUs, 20th overall pick being worth ~7.0, etc.)

In order to test validity, we can compare this curve/equation to those derived by Shuckers and Burtch. The similarities between the three draft value methodologies help to support the accuracy of the findings of each. Note – the one downfall of this curve is that, in order for it to appropriately reflect the value of the first 100 picks, the DVU’s hit zero around pick 100. As a result, my advice for those trying to use this to value picks from the 4th Round and onward is to treat each pick as having a Draft Value of 1.0 units, rather than zero (e.g. revert back to the dotted line).

Now – back to Leafs v. Philadelphia.

Plotting the three Leafs/Flyers picks traded on our curve shows the value of each individually

 DVU Curve - TOR PHL

  • The chart above shows that the theoretical value of the 24th overall pick is 6.0 DVUs, with the 29th and 61st overall being worth 5.2, and 2.3, respectively
  • As I did here, in order to use this on any other trade, all you have to do is find each pick number on the x-axis, trace it to the curve, and then trace that point on the curve back to the y-axis – giving you the Draft Value Units of that pick
    • The end of this article also has a table showing Draft Value Units for each pick number, e.g. the coordinates that make up this line

On an expected value basis, the Leafs won the trade with Philly by 25%

TOR PHL - Stacked Bar

  • Based on the values assigned above, the Leafs were the clear winners of this trade
  • As a reminder – these Draft Value Units originate based on an average of the probabilities for each player drafted to do three things: 1) Exceed 30 career points, 2) Exceed 100 career points, and 3) Maximize their lifetime (point) production

One last important thing to point out – as I’m sure many Flyers fans and general non-stats folks will want to discuss – the Flyers executed this trade because they badly wanted to pick Travis Konecny, of recent Canadian World Junior ‘fame’. They saw him as materially better than their next choice (if they had picked 29th) – Nick Merkley. This piece isn’t about scouting or individual player evaluation, which are of course important factors to consider – generally speaking, every team should be drafting with a broad strategy in mind, that drives towards filling its specific needs (which I’m sure justified this trade from the Flyers’ perspective). The point of this analysis is to say that – on a long term, expected value/probability basis – a team will do better to be on the Leafs’ side of this trade. Even if 10 years from now Konecny is the next John Tavares, and everyone thinks Ron Hextall is a genius – I think the Leafs were on the right side of this trade based on what was known on draft day.

(As a side note: this article by Travis Hughes gives a bit of background about Philadelphia’s rationale for being so eager to trade up for Travis Konecny).

So far, Leafs 1, League 0

Draft Day 2015 Deal #2 – Dubas and Hunter v. Jarmo Kekalainen of the Columbus Blue Jackets

 It didn’t take long for Dubas and Hunter to turn around and offload their newly acquired 29th overall pick either – employing a highly similar strategy in their trade with Columbus:

TOR CLB Trade Chart

Now if we apply the same analysis to this deal:

Looking at the value of each pick individually…

DVU Curve - TOR CLB

… It is clear the Leafs ‘won’ this deal too – by 23%

TOR COL - Stacked Bar

You don’t need me to tell you too much more, as the same analytical framework shows the Leafs fared similarly well in their trade with Columbus as they did with Philadelphia.

Before I wrap up, just for fun let’s look at the two deals as whole:

TOR Aggregate Trade Chart

The Leafs’ two trades during the 2015 draft created an incremental two players, 2.7 draft value units, or otherwise a 43% increase in relative value

TOR Aggregate Stacked Bar

By the end of the draft, the Leafs had used these three picks to select Travis Dermott, Jeremy Bracco, and Martins Dzierkals, based on the scouting expertise employed by Hunter and his team. It also helped fill some of the team’s draft objectives early on, enabling them to wait it out for sleeper picks like Dmytro Timashov in the fifth round – a player who turned many heads at the recent World Juniors tournament. I wouldn’t (yet) go as far as to say that Hunter has a legitimate ‘competitive advantage’ over other teams in player scouting and evaluation until more time has passed. However, Dermott’s OHL performance this year and recent World Junior selection also suggest that getting him at 34th was a also bit of a steal in its own right. Regardless, it should be clear that on this fateful day last June, Kyle Dubas and Mark Hunter made some excellent decisions, which created a ton of value and long term potential for the Toronto Maple Leafs club.

Also – I won’t go into it in depth here – but how did the Leafs acquire that original 24th overall pick? The Leafs got it by trading two ‘rental’ players (Franson/Santorelli), who were both about to become UFA’s, in return for that 1st Round pick, Brandon Leipsic (another solid prospect), and Olli Jokinen’s cap space. Keep an eye out for the Leaf’s front office to hopefully make a couple similar deals approaching the trade deadline this year, and repeat their excellent 2015 performance next June.

Leafs 2, League 0

Conclusion

When considering teams that have been successful in the NHL draft, the teams that come to mind intuitively support the findings of this analysis. The Chicago Blackhawks are a great example where their distinct strategy has been ‘quantity over quality’: rather than trying to pick ‘better’, as has been shown to be very difficult to do, Chicago has simply focused on using transactions like these to draft as many players as possible. Chicago’s massive, league leading number of picks from 2000-2004 show that the Hawks certainly planted their seeds – and in case anyone has been paying attention, they have been doing ‘OK’ in the last 5-10 years. Another great example of this strategy is Bill Belichick and the New England Patriots – who seem to have done alright in the last 10 or so years as well…

In the end – as one of the small but growing number of patient, excited Leafs fans out there, I will make our collective opinion clear: the current front office knows what they are doing – and we are behind them.

Appendix Table

What is an NHL Draft Pick Really Worth?

A Detailed Analysis of Player Performance and Development by Draft Round

It is hard to describe drafting as anything less than essential to the success of a professional sports franchise. The teams that are able to plant the seeds for long term success on draft day each year will have a clear advantage over the five to ten years that follow – if not sooner. We all know that every NHL franchise has an expert scouting team, and many likely use metrics like league equivalencies to see what they are getting from a pick – but how many teams know what to expect from a player’s long term performance based on the round they drafted him? Further, how would that knowledge impact how a team assigns ‘value’ to its future picks in trades?

In order to answer these, and other questions, I have dug into some of the data available, and summarized into a report that you can find hereWarning – it is long. In this ‘report’, I try to answer the two questions above, as well as a more detailed list of questions that you can find at the bottom of the page.  Quickly, thanks to Hockey-Reference.com for the draft year data, and HockeyAbstract.com for the historical NHL season data.

As the first piece of work I am releasing to the NHL analytics world, the most basic reason that I have focused on draft analysis is because I personally am very curious about it. Like many fans, I often find myself looking at the exciting picks selected each year, but previously I had not quantitatively understood what to expect from those players in the NHL – specifically on a long term, year by year basis, generalized to the round they were drafted in. Before starting to analyze some of the data available, I also didn’t have a full appreciation of how a team should be treating the long term potential value behind those picks versus what else is out there (though some great work has been done on that in the past, by Stephen Burtch and Michael Shuckers, amongst others).

Lastly, I also think draft analysis has a number of strong parallels to the analysis done in value investing (aka the purpose of this blog). When an investor is evaluating a potential company to buy, the major focus is on its growth and earnings potential over a very long term time horizon, e.g. 4-7 years, or more. Much like ‘investing’ in the potential, growth and long term development of a player, the same type of thought process needs to be followed when buying a company. Investors operate with scarce resources and other constraints (e.g. contract limits, salary caps), they must have a clear understanding of their own strategy and needs (e.g. immediate cup contender vs. rebuild scenario), they have to undergo prioritization by evaluating the alternatives against a set of criteria (e.g. prioritizing positions, ranking players in each), and ultimately they have to make a decision on how to move forward – a decision that they will often have to live with for half a decade or more (in the case of private companies).

In investing, as in drafting to help build an NHL organization – the more detailed, understandable, and accurate quantitative information you have to support your decisions, the more likely you are to be successful. Thus, as the NHL enters the months before the trade deadline, I hope the attached analysis can give the online community some food for thought as to who the winners and losers are of trades to come. Please let me know any comments, questions, feedback or areas for further analysis at @OrgSixAnalytics or OriginalSixAnalytics@gmail.com.

 

This article is presented by OAK Coasters, a website where you can by beautifully crafted, hand made One of A Kind (OAK) coasters that make the perfect gift.  Check them out at OAKCoasters.com.

 

For reference, the analysis seeks to answer the following questions:

  • If a player is drafted in round X, and is ultimately able to make the NHL, by when should they be expected to be a contributing NHL player?
  • How well does the typical player perform over the course of his career (on various metrics) after being selected in a given round? Within the first round, how do the top 10 overall picks perform versus those taken 11th-30th?
  • How much more valuable is a pick in the first round versus the other rounds? All things being equal, what should a pick from each round be worth in a trade?
  • Which teams were the most effective at drafting in the period sampled?